R Programming Basics for Data Science and Machine Learning

Name: R Programming Basics for Data Science and Machine Learning
Rating: 4.6 (171 reviews)

Learn Latest R 4 with R-Studio & Jupyter. DataFrame, Vectors, Matrix, DateTime, GGplot2, Tidyverse, Plotly, etc.

Created byKGP Talkie | Laxmi Kant

Last updated 10/2025

English

What you'll learn

Learn to write a program in R 4.0
Learn fundamentals of R programming
How to use R-Studio
How to analyze the data
How to plot beautiful plots
Real exercise for data analysis
Use for Machine Learning programming
Write code for Linear Regression and Logistic Regression Analysis
Data visualization on real dataset | Covid-19, Boston Housing Price and Titanic dataset
Learn Plotly for Covid-19 Data Analysis
Advanced Plotly in R
Linear Regression in R
Non-Linear and Polynomial Regression
Multiple Simple Linear Regression in R on Boston Housing Price Prediction

Course content

8 sections • 126 lectures • 14h 17m total length

Install R and R-Studio for Data Science3:32
Course Introduction!!!
Download Code Files || Do Not Skip This!!!0:47
Get code files here!!!
R-Studio Introduction8:21

Variable Assignments4:44
I will be showing how to assign values to R objects. I will be also discussing the rules of variable assignment in R Programming.
Rules of Variable Names in R8:10
Arithmetic Operators4:24
Relational Operators4:46
Logical Operators9:05
Assignment Operators2:17
Miscellaneous Operators5:48
Function in R6:39
Data Types in R6:37
Strings Assignment4:28
paste() Function for String Manipulation3:53
format() Function for Numeric Data Formatting3:34
Colon (:) Operator for Vector Generation6:48
Using [] Operator and c() Function to Access Vector Elements3:48
Vector Manipulation7:11
List Creation3:35
Named List3:27
List Manipulation and Merging5:13
List to Vectors and Vectors to List3:58
Introduction to Matrix6:04
Arithmetic Operations on Matrix6:09
Arrays Introductions4:41
Arrays Naming and Accessing the Values10:33
Factors in R6:43
If If-Else and If-Else-If Statements6:53
repeat() and while() Loops6:22
for() Loop4:49
next Statement and break Statement4:35

Create DataFrame in R8:09
Get the DataFrame Details6:21
Working with [, [[ and $ Operator3:44
Access DataFrames Like Matrix5:46
Modify a DataFrame4:34
Loading a DataFrame from .CSV File6:00
Load DataFrame from Excel .xlsx File5:02
Loading a DataFrame from .XML File6:09
Loading a DataFrame from .json File4:52
Bind Rows || rbind() and bind_rows()7:39
Bind Columns || cbind() and bind_cols()2:47
Data Frame Selection and Indexing4:47
Conditional DataFrame Selection with subset()7:09
Apply conditional selection with subset() to filter data by country, identify the maximum confirmed cases and their dates, and understand cumulative sums versus new daily cases.
Working with DateTime in DataFrame8:45
Export DataFrame in .CSV File3:37
Data Frame Sorting2:08
Groupby on DataFrame in R8:52
Data Frame Merge and Join || Inner Join12:40
Left, Right, and Outer Merge (Join) of DataFrame in R8:06
Master outer, left, and right joins in R to merge data frames, fill nulls for missing matches, and understand how union behavior shapes the combined data across keys.

Jupyter Notebook Introduction5:51
Anaconda Installation for Windows 106:04
Anaconda Installation for Linux3:24
R 4.x Installation in Anaconda with Jupyter Notebook12:56
Jupyter Notebook Shortcuts Part 18:30
Jupyter Notebook Shortcuts Part 29:13
Jupyter Notebook Shortcuts Part 310:25
Jupyter Notebook Shortcuts Part 45:43
Master Jupyter notebook shortcuts for markdown, including embedding links and images, creating headings, tables, footnotes, task lists, and horizontal rules, with hints from a markdown cheat sheet on GitHub.
R Coding Practice with Jupyter Notebook vs R-Studio10:40

Introduction to GGPlot29:33
R Packages Installation and Loading6:17
Must Read0:28
Covid-19 Dataset Loading14:02
Bar Plot - Top 10 Worst Hit Countries8:44
Add Title, Subtitle and Caption in GGPlot6:58
Learn how to add a title, subtitle, and caption to a ggplot, customize fonts with element_text, adjust alignment with hjust, and explore labs vs ggtitle methods.
Change Title and Caption Style- Font Size, Color and Face4:22
Change Text Position and Increase Figure Size7:46
Scatter Plot (Point Plot) for Covid-19 Dataset7:54
Line Plot for Covid-19 Data || Confirmed, Recovered and Deaths Analysis9:26
Loading the Boston Housing Price Dataset for Visualization7:40
Scatter Plot for Boston Housing Data4:53
Pair Plot - Scatter Matrix Plot for Boston Housing Dataset10:00
Load Titanic Dataset for Visualization6:03
Data Cleaning and Bar Plot9:37
Scatter Plot for Titanic Dataset6:41
Histogram Plot6:21
Stacked Histogram7:56
Density Plot7:23
Box Plot7:14
Violin Plot4:21
Plot the violin to visualize how a continuous variable distributes across categories, revealing age density and survival patterns by gender and passenger class.

Jupyter Notebook Opening1:41
Getting Started with tidyverse and dplyr6:07
Must Read0:28
select() - Select Columns of a Dataframe17:47
Learn how the select() method picks specific columns from a data frame, drops others with minus, and filters by starts with or ends with names in the covid-19 dataset.
filter() || Extract subset of Rows7:16
arrange() - DataFrame Sorting4:37
rename() || Renaming DataFrame Columns2:18
mutate() || Compute Transformations of Variables10:26
group_by() || Group DataFrame Column-wise13:37
%>% || Pipeline Operator5:27
distinct() || Get the Unique Rows4:31
count() tally() add_count() add_tally() || Count the Unique Values in DataFrame7:10
rename_with() || Rename Columns by using Function5:49
summarise() and summarize() || Create Summary of Columns in a DataFrame5:22
summarise_at(), summarise_if(), and summarise_all() || Auto Summary of Columns7:06
slice() and slice_head() || Slice DataFrame Row-wise6:23
between() || Shortcut for x >= left and x <= right2:07
cumall(), cumany(), and cummean() || Cumulative Logical Functions5:03

Plotly Introduction6:57
Load Covid-19 Dataset6:42
Line Plot() - Daywise Corona Cases13:08
Style Line Plots9:53
Marker Styling in R7:44
Bar Chart - Top10 Worst Hit Countries10:55
Bar Chart with Direct Labels5:23
Customize Individual Bar Colors5:10
subplots() - Complete Cases Analysis for USA10:59
Daywise Case Analysis with Subplot()11:50
Scatter Plot for Deaths vs Confirmed Cases5:35
Pie Chart5:40
Donut Chart1:52

What is Machine Learning8:29
Explore the fundamentals of machine learning, supervised, unsupervised, reinforcement, and semi-supervised, along with regression, classification, and clustering, plus end-to-end workflows from data to deployment.
What is Linear Regression3:53
Explore linear regression through house price prediction on a Boston housing dataset, learn supervised learning concepts, independent versus dependent variables, and the fitted regression line.
How Linear Regression Model Works3:28
Where You Can Use Regression Model5:25
Types of Linear Regression5:49
Identify simple and multiple linear regression, using one or more input variables to predict house prices, and interpret the loss function to reach minimum error on a plane or line.
Variables Correlation Analysis8:00
Crime Analysis in Boston7:17
Top 10 percentile Crim Area Analysis11:24
Portion of Population with Lower Status Analysis4:26
Top 10 percent Portion of Population with Lower Status Analysis8:17
Build and Train Simple Linear Regression Model15:58
Null and Alternate Hypothesis - p-Value and t-Value Explained14:03
R-Squared and Adjusted R-Squared7:43
Polynomial Regression Model15:59
Multiple Variable Simple Linear Regression10:34

Requirements

Anyone interested to become a Data Scientist or Data Analyst
No prerequisites require

Description

Take your first step towards becoming a data science expert with our comprehensive R programming course. This course is designed for beginners with little or no programming experience, as well as experienced R developers looking to expand their skill set.

You'll start with the basics of R programming and work your way up to advanced techniques used in data science. Along the way, you'll gain hands-on experience with popular R libraries such as dplyr, ggplot2, and tidyr.

You will learn how to import, clean and manipulate data, create visualizations and statistical models to gain insights and make predictions. You will also learn data wrangling techniques and how to use R for data visualization.

By the end of the course, you'll have a solid understanding of R programming and be able to apply your new skills to a wide range of data science projects. You'll also learn how to use R in Jupyter notebook, so that you can easily share your work and collaborate with others.

So, if you're ready to take your first step towards becoming a data science expert, this is the course for you! With our hands-on approach and interactive quizzes, you'll be able to put your new skills into practice right away.

In this course, you learn:

How to install R-Packages
How to work with R-data types
What is R DataFrame, Matrices, Vectors, etc?
How to work with DataFrames
How to perform join and merge operations on DataFrames
How to plot data using ggplot2 in R 4
Analysis of real-life dataset Covid-19

How this course will help you?

This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models. If you are a business manager or an executive or a student who wants to learn and excel in machine learning, this is the perfect course for you.

Who this course is for:

Data Scientist Beginners
R Programmers
Data Scientist who codes in R
Data Analyst who codes in R
Data Scientist managers, executives or students

R Programming Basics for Data Science and Machine Learning

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 13min

R Programming Fundamentals28 lectures • 2hr 35min

Fundamentals of DataFrames in R Programming19 lectures • 1hr 57min

Jupyter Notebook Introduction for R Programming9 lectures • 1hr 13min

Fundamentals of Data Visualization with GGPlot221 lectures • 2hr 34min

Data Preprocessing and Analysis with tidyverse and dplyr18 lectures • 1hr 53min

Plotly | Covid-19 Data Analysis13 lectures • 1hr 42min

Linear Regression and Data Analysis on Boston Housing Dataset15 lectures • 2hr 11min

Requirements

Description

Who this course is for: